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EPJ B Highlight - A novel computing approach to recognising chaos
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- Published on 05 April 2022
Chaos isn’t always harmful to technology, in fact, it can have several useful applications if it can be detected and identified.
Chaos and its chaotic dynamics are prevalent throughout nature and through manufactured devices and technology. Though chaos is usually considered a negative, something to be removed from systems to ensure their optimal operation, there are circumstances in which chaos can be a benefit and can even have important applications. Hence a growing interest in the detection and classification of chaos in systems.
A new paper published in EPJ B authored by Dagobert Wenkack Liedji and Jimmi Hervé Talla Mbé of the Research unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, University of Dschang, Cameroon, and Godpromesse Kenné, from Laboratoire d’ Automatique et d’Informatique Appliquée, Department of Electrical Engineering, IUT-FV Bandjoun, University of Dschang, Cameroon, proposes using the single nonlinear node delay-based reservoir computer to identify chaotic dynamics.
EPJ E Highlight - The relationship between active areas and boundaries with energy input in snapping shells
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- Published on 05 April 2022
New research looks at how the geometry of shells relates to the energy input required to actuate snap-through instability.
In nature, diverse organisms such as the hummingbird and Venus flytrap use rapid snapping motions to capture prey, inspiring engineers to create designs that function using snap-through instability of shell structures. Snapping rapidly releases stored elastic energy and does not require a continuously applied stimulus to maintain an inverted shape in bistable structures.
A new paper published in EPJ E authored by Lucia Stein-Montalvo, Department of Civil and Environmental Engineering, Princeton University, and Douglas P. Holmes, Department of Mechanical Engineering, Boston University, along with co-authors Jeong-Ho Lee, Yi Yang, Melanie Landesberg, and Harold S. Park, examines how restricting the active area of the shell boundary allows for a large reduction in its size, and decreases the energy input required to actuate snap-through behaviour in the shell to guide the design of efficient snapping structures.
EPJ B Highlight - Investigating newly synthesised thallium compounds for optoelectronic devices
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- Published on 01 April 2022
The burgeoning field of optoelectronic devices is driving the development of new alkali metal-based chalcogenides with qualities that have to be robustly investigated.
The need for efficient optoelectronic devices is growing and hand-in-hand so too is the challenge of discovering new semiconductors with valuable properties. This has spurred significant research in the synthesis and characterization of new alkali metal-based (AM) chalcogenides involving copper, silver and alkali metal with valuable properties like flexibility, high thermal stability, semiconductivity, photovoltaic effects.
Inspired by the growing demand for new optimum semiconducting materials, a new paper published in EPJ B authored by Abdelmadjid Bouhemadou, Laboratory for Developing New Materials and their Characterizations, Department of Physics, Faculty of Science, University of Ferhat Abbas Setif, Algeria and his co-authors, investigated in detail the structural, elastic, electronic and optical properties of two newly synthesized compounds, namely Tl2CdGeSe4 and Tl2CdSnSe4.
EPJ D Highlight - Astrophysical plasma study benefits from new soft X-ray transition energies benchmark
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- Published on 01 April 2022
The new benchmark for X-ray transition energies set for neon, carbon dioxide, and sulfur hexafluoride paves a pathway to high accuracy analysis of astrophysical plasmas.
The analysis of astrophysical plasmas is vital in the quest to learn about some of the Universe’s most powerful and mysterious objects and events such as stellar coronae and winds, cataclysmic variables, X-ray binaries containing neutron stars and black holes, supernova remnants, or outflows in active galactic nuclei. The success of such research will lead to future astrophysical X-ray observatories enabling scientists to access techniques that are currently not available to X-ray astronomy. A key requirement for the accurate interpretation of high-resolution X-ray spectra is accurate knowledge of transition energies.
A new paper published in EPJ D authored by J. Stierhof, of the Dr. Karl Remeis-Observatory and Erlangen Centre for Astroparticle Physics of Friedrich-Alexander-Universt Erlangen-Nürnberg, Bamberg, Germany, and coauthors utilizes a newly introduced experimental setup at the BESSY II synchrotron facility to provide precise calibration references in the soft X-ray regime of neon, carbon dioxide, and sulfur hexafluoride gases.
EPJ H Highlight - Documenting the first attempt at a gravitational-wave observatory in Europe
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- Published on 21 March 2022
EUROGRAV was set to be a network of gravitational wave antennas in Europe. A new paper looks at the reasons it never happened.
First predicted in Einstein’s theory of general relativity, gravitational waves are tiny ripples in spacetime generated by titanic and powerful cosmic events. The great physicist believed that no equipment would ever be sensitive to detect these faint cosmic ripples. Fortunately, Einstein was wrong, but that doesn’t mean that the detection of gravitational waves has been easy.
The history of a planned array interferometer gravitational wave detectors to be built in Europe during the late 1980s, the reasons this failed, and the parallels with current detectors, are documented in a new paper published in EPJ H, authored by Adele La Rana, University of Verona, and INFN Section of Sapienza University, Italy.
EPJ Plus Highlight - Tackling large data sets and many parameter problems in particle physics
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- Published on 21 March 2022
A new tool to break down and segment large data set problems and problems with many parameters in particle physics could have a wide range of applications.
One of the major challenges in particle physics is how to interpret large data sets that consist of many different observables in the context of models with different parameters.
A new paper published in EPJ Plus, authored by Ursula Laa from the Institute of Statistics at BOKU University, Vienna, and German Valencia from the School of Physics and Astronomy, Monash University, Clayton, Australia, looks at the simplification of large data set and many parameter problems using tools to split large parameter spaces into a small number of regions.
EPJ H Highlight - Acknowledging Fermi’s contributions to early quantum statistics
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- Published on 18 March 2022
Enrico Fermi’s ideas played a key role in the origins of quantum statistics, but so far, they have been largely overlooked in historical analysis
Within large systems of identical fermions, Fermi-Dirac statistics describes how identical fermions may never occupy the same quantum state. First introduced by Italian physicist, Enrico Fermi, this concept was a key step in our early understanding of quantum mechanics – yet so far, Fermi’s contributions have been largely overlooked in historical analysis. Through new research published in EPJ H, Enric Pérez and Joana Ibáñez, both at the University of Barcelona, Spain, offer a critical analysis of Fermi’s ideas, and assess their immediate impact on our early conceptions of quantum mechanics.
EPJ H Highlight - Assessing the modern relevance of Schrödinger’s time reversal
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- Published on 18 March 2022
Erwin Schrödinger’s landmark 1931 paper provided a basis for several important conceptions in quantum mechanics, but a new translation and commentary highlights its continuing relevance in modern statistical nanophysics
In 1931, Erwin Schrödinger published a ground-breaking paper, named ‘On the Reversal of the Laws of Nature.’ The study aimed to prove the possibility of a classical structure governed by probability, which displays a property called ‘time reversal symmetry’: where the physical laws underlying the system would remain the same, whether time flowed forwards or backwards. A new English translation of Schrödinger’s paper, published in EPJ H, has now been made by Raphael Chetrite at the University of Nice Sophia Antipolis; Paolo Muratore-Ginanneschi at University of Helsinki; and Kai Schwieger at iteratc GmbH Stuttgart. In an additional commentary, the team emphasise the relevance of his intuitions for modern developments in statistical nanophysics.
EPJ Plus Focus Point on Rewriting Nuclear Physics Textbooks: Recent Advances in Nuclear Physics Applications
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- Published on 18 March 2022
Guest Editors: Nicolas Alamanos, Carlos Bertulani, Angela Bonaccorso, Angela Bracco, David M. Brink, Giovanni Casini, Maria Agnese Ciocci, Valeria Rosso & Michele Viviani
This collection of articles contains some of the lectures presented at the Summer School ``Re-writing Nuclear Physics textbooks: recent advances in nuclear physics applications" which was held at the INFN Sezione di Pisa and Department of Physics of the University of Pisa in July 2019. The School followed two previous editions dedicated to "30 years with Radioactive Ion Beam Physics" and "Basic Nuclear Interactions and Their Link to Nuclear Processes in the Cosmos and on Earth" also held at the same place in July 2015 and 2017 respectively.
EPJ C Highlight - A cautionary tale of machine learning uncertainty
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- Published on 07 March 2022
By decorrelating the performance of machine learning algorithms with imperfections in the simulations used to train them, researchers could be estimating uncertainties that are lower than their true values.
The Standard Model of particle physics offers a robust theoretical picture of the fundamental particles, and most fundamental forces which compose the universe. All the same, there are several aspects of the universe: from the existence of dark matter, to the oscillating nature of neutrinos, which the model can’t explain – suggesting that the mathematical descriptions it provides are incomplete. While experiments so far have been unable to identify significant deviations from the Standard Model, physicists hope that these gaps could start to appear as experimental techniques become increasingly sensitive.
A key element of these improvements is the use of machine learning algorithms, which can automatically improve upon classical techniques by using higher-dimensional inputs, and extracting patterns from many training examples. Yet in new analysis published in EPJ C, Aishik Ghosh at the University of California, Irvine, and Benjamin Nachman at the Lawrence Berkeley National Laboratory, USA, show that researchers using machine learning methods could risk underestimating uncertainties in their final results.
